Technical Field
Embodiments of the present disclosure generally relate to the field of power grid management technology and more particularly to a real-time data management system, a system, method, apparatus and tangible computer readable medium for accessing data in a power grid, a system, method, apparatus and tangible computer readable medium for controlling a transmission delay of real-time data delivered via a real-time bus, and a system, method, apparatus and tangible computer readable medium for delivering real-time data in a power grid.
Background
Various industries have networks associated with them. One such industry is the utility industry that manages a power grid. The power grid may include one or all of the following: electricity generation, electric power transmission, and electricity distribution. Electricity may be generated using generating stations, such as a coal fire power plant, a nuclear power plant, etc. For efficiency purposes, the generated electrical power is stepped up to a very high voltage (such as, for example, 345K Volts) and transmitted over transmission lines. The transmission lines may transmit the power long distances, such as across state lines or across international boundaries, until it reaches its wholesale customer, which may be a company that owns the local distribution network. The transmission lines may terminate at a transmission substation, which may step down the very high voltage to an intermediate voltage (such as, for ex ample, 138K Volts). From a transmission substation, smaller transmission lines (such as, for example, sub-transmission lines) transmit the intermediate voltage to distribution substations. At the distribution substations, the intermediate voltage may be again stepped down to a “medium voltage” (such as, for example, from 4K Volts to 23K Volts). One or more feeder circuits may emanate from the distribution substations. For example, four to tens of feeder circuits may emanate from the distribution substation. The feeder circuit is a 3-phase circuit comprising 4 wires (three wires for each of the 3 phases and one wire for neutral). Feeder circuits may be routed either above ground (on poles) or underground. The voltage on the feeder circuits may be tapped off periodically using distribution transformers, which step down the voltage from “medium voltage” to the consumer voltage (such as, for example, 120V). The consumer voltage may then be used by the consumers.
One or more power companies whose main responsibility is to supply reliable and economic electricity to their customers. These power companies may manage the power grid, including planning, operation, and maintenance related to the power grid. However, the management of the power grid is often inefficient and costly. For example, a power company that manages the local distribution network may manage faults that may occur in the feeder circuits or on circuits, called lateral circuits, which branch from the feeder circuits. The management of the local distribution network often relies on telephone calls from consumers when an outage occurs or relies on field workers patrolling and monitoring the local distribution network.
Power companies have attempted to upgrade the power grid to be a “smart grid” by applying the state-of-the-art IT and power engineering technologies. With the development of the smart grid, a large number of utilities are deploying Advanced Metering Infrastructure (AMI), Phase Measurement Unit (PMU) and other online monitoring equipment. These equipments provide different data to applications and may adhere to different latency requirements. For example, the PMU has a data frequency of 20 to 50 milliseconds; a Supervisory Control And Data Acquisition/Energy Management System (SCADA/EMS) has a data frequency of 1 to 5 seconds; the AMI has a data frequency of 5 to 15 minutes; device monitoring system has a data frequency of 1 to 5 minutes; and an event should be notified immediately when the event happens. Moreover, each of devices will produce a large amount of data. This means that a large amount of data is generated from different originations, business units and systems.
FIG. 1 schematically illustrates various latency levels of data in an example existing power system. As illustrated in FIG. 1, data acquired from sensor devices will be directly provided to a high speed/real-time analytical application with a latency that ranges from milliseconds to seconds; the acquired data will be stored in a real-time or sub real-time database for a purpose of medium speed/real-time analytical with a latency that ranges from seconds to minutes; the acquired data will be also stored in a database for historical data for using by a transaction analytical application with a latency that ranges from minutes to days; the acquired data are further stored in a business data repository for using by a business intelligence application with a latency that ranges from days to months. It can be seen that these data are time series continuous data with different latency varying from milliseconds to months and they are constantly increasing. Additionally, measurement objects are continuously increasing and large events might also be suddenly generated in a very short period of time when a contingency occurs, which all will rapidly increase the data volume. This constantly increasing data have become a challenge for the power system management, and hence there is a critical demand for efficiently managing the high volume of data in the art.